private CvSVM loadSVM()
{
string myPhotos = Environment.GetFolderPath(Environment.SpecialFolder.MyPictures);
string folderPath = Path.Combine(myPhotos, "shape_samples");
CvSVM svm = new CvSVM();
svm.Load(Path.Combine(folderPath, "trained_svm"));
return(svm);
}
// Use this for initialization
void Start()
{
PXCUPipeline.Mode mode = Options.mode & (~PXCUPipeline.Mode.VOICE_RECOGNITION);
if (mode == 0)
{
return;
}
pp = new PXCUPipeline();
if (!pp.Init(mode))
{
print("Unable to initialize the PXCUPipeline");
return;
}
plane = GameObject.Find("Plane");
pp.QueryRGBSize(RGBMapSize);
if (RGBMapSize[0] > 0)
{
Debug.Log("rgb map size: width = " + RGBMapSize[0] + ", height = " + RGBMapSize[1]);
rgbTexture = new Texture2D(RGBMapSize[0], RGBMapSize[1], TextureFormat.ARGB32, false);
// use the rgb texture as the rendered texture
plane.renderer.material.mainTexture = rgbTexture;
pp.QueryDepthMapSize(depthMapSize);
if (depthMapSize[0] > 0)
{
Debug.Log("depth map size: width = " + depthMapSize[0] + ", height = " + depthMapSize[1]);
depthStorage = new short[depthMapSize[0] * depthMapSize[1]];
}
pp.QueryUVMapSize(uvMapSize);
if (uvMapSize[0] > 0)
{
Debug.Log("uv map size: width = " + uvMapSize[0] + ", height = " + uvMapSize[1]);
uvStorage = new short[uvMapSize[0] * uvMapSize[1] * 2];
}
}
cascade = CvHaarClassifierCascade.FromFile(@"./Assets/haarcascade_frontalface_alt.xml");
/*capture = Cv.CreateCameraCapture(0); //bentrok dgn pxcupipeline
* Cv.SetCaptureProperty(capture, CaptureProperty.FrameWidth, CAPTURE_WIDTH);
* Cv.SetCaptureProperty(capture, CaptureProperty.FrameHeight, CAPTURE_HEIGHT);
* IplImage frame = Cv.QueryFrame(capture);
* Debug.Log("width:" + frame.Width + " height:" + frame.Height);*/
Cv.NamedWindow("FaceDetect");
CvSVM svm = new CvSVM();
CvTermCriteria criteria = new CvTermCriteria(CriteriaType.Epsilon, 1000, double.Epsilon);
CvSVMParams param = new CvSVMParams(CvSVM.C_SVC, CvSVM.RBF, 10.0, 8.0, 1.0, 10.0, 0.5, 0.1, null, criteria);
}
// Use this for initialization
void Start()
{
cascade = CvHaarClassifierCascade.FromFile(@"./Assets/haarcascade_frontalface_alt.xml");
capture = Cv.CreateCameraCapture(0);
Cv.SetCaptureProperty(capture, CaptureProperty.FrameWidth, CAPTURE_WIDTH);
Cv.SetCaptureProperty(capture, CaptureProperty.FrameHeight, CAPTURE_HEIGHT);
IplImage frame = Cv.QueryFrame(capture);
Cv.NamedWindow("FaceDetect");
CvSVM svm = new CvSVM();
CvTermCriteria criteria = new CvTermCriteria(CriteriaType.Epsilon, 1000, double.Epsilon);
CvSVMParams param = new CvSVMParams(CvSVM.C_SVC, CvSVM.RBF, 10.0, 8.0, 1.0, 10.0, 0.5, 0.1, null, criteria);
}
private float classifyShape(Mat shape, CvSVM svm)
{
HOGDescriptor hog = new HOGDescriptor();
hog.WinSize = new OpenCvSharp.CPlusPlus.Size(32, 32); // Set hog features here: winSize; blockSize; cellSize
hog.BlockSize = new OpenCvSharp.CPlusPlus.Size(4, 4);
hog.CellSize = new OpenCvSharp.CPlusPlus.Size(4, 4);
hog.BlockStride = new OpenCvSharp.CPlusPlus.Size(2, 2);
float[] features = hog.Compute(shape, new OpenCvSharp.CPlusPlus.Size(16, 16), new OpenCvSharp.CPlusPlus.Size(0, 0), null);
hog.Dispose();
Mat featureMat = new Mat(1, features.Length, MatType.CV_32FC1, features);
float prediction = svm.Predict(featureMat.ToCvMat());
featureMat.Dispose();
return(prediction);
}
/// <summary>
/// Initializes a new instance of the MainWindow class.
/// </summary>
public MainWindow()
{
this.shapeSVM = loadSVM(); //load SVM
this.kinectSensor = KinectSensor.GetDefault(); // get the kinectSensor object
this.multiSourceFrameReader = this.kinectSensor.OpenMultiSourceFrameReader(FrameSourceTypes.Color | FrameSourceTypes.Depth | FrameSourceTypes.BodyIndex);
this.multiSourceFrameReader.MultiSourceFrameArrived += MultiSourceFrameReader_MultiSourceFrameArrived;
this.coordinateMapper = this.kinectSensor.CoordinateMapper;
this.depthFrameDescription = this.kinectSensor.DepthFrameSource.FrameDescription;
this.colorFrameDescription = this.kinectSensor.ColorFrameSource.FrameDescription;
this.colorBitmap = new WriteableBitmap(this.colorFrameDescription.Width, this.colorFrameDescription.Height, 96.0, 96.0, PixelFormats.Bgr32, null); // create the bitmap to display
this.depthMappedToColorPoints = new ColorSpacePoint[this.depthFrameDescription.LengthInPixels];
this.depthPixels = new byte[this.depthFrameDescription.Width * this.depthFrameDescription.Height];
int bytesPerPixel = (PixelFormats.Bgr32.BitsPerPixel + 7) / 8;
// Calculate the WriteableBitmap back buffer size
this.bitmapBackBufferSize = (uint)((this.colorBitmap.BackBufferStride * (this.colorBitmap.PixelHeight - 1)) + (this.colorBitmap.PixelWidth * bytesPerPixel));
this.kinectSensor.IsAvailableChanged += this.Sensor_IsAvailableChanged; // set IsAvailableChanged event notifier
this.kinectSensor.Open(); // Open the sensor
this.fpsText = "FPS = 0";
this.outputText = "No state";
this.StatusText = this.kinectSensor.IsAvailable ? Properties.Resources.RunningStatusText : Properties.Resources.NoSensorStatusText; // set the status text
this.DataContext = this; // use the window object as the view model in this simple example
this.InitializeComponent(); // initialize the components (controls) of the window
if (!this.kinectSensor.IsAvailable)
{
loadFakeKinect();
}
}
public void Run()
{
// Training data
var points = new CvPoint2D32f[500];
var responses = new int[points.Length];
var rand = new Random();
for (int i = 0; i < responses.Length; i++)
{
double x = rand.Next(0, 300);
double y = rand.Next(0, 300);
points[i] = new CvPoint2D32f(x, y);
responses[i] = (y > f(x)) ? 1 : 2;
}
// Show training data and f(x)
using (Mat pointsPlot = Mat.Zeros(300, 300, MatType.CV_8UC3))
{
for (int i = 0; i < points.Length; i++)
{
int x = (int)points[i].X;
int y = (int)(300 - points[i].Y);
int res = responses[i];
Scalar color = (res == 1) ? Scalar.Red : Scalar.GreenYellow;
pointsPlot.Circle(x, y, 2, color, -1);
}
// f(x)
for (int x = 1; x < 300; x++)
{
int y1 = (int)(300 - f(x - 1));
int y2 = (int)(300 - f(x));
pointsPlot.Line(x - 1, y1, x, y2, Scalar.LightBlue, 1);
}
Window.ShowImages(pointsPlot);
}
// Train
var dataMat = new Mat(points.Length, 2, MatType.CV_32FC1, points);
var resMat = new Mat(responses.Length, 1, MatType.CV_32SC1, responses);
using (var svm = new CvSVM())
{
// normalize data
dataMat /= 300.0;
var criteria = TermCriteria.Both(1000, 0.000001);
var param = new CvSVMParams(
SVMType.CSvc,
SVMKernelType.Rbf,
100.0, // degree
100.0, // gamma
1.0, // coeff0
1.0, // c
0.5, // nu
0.1, // p
null,
criteria);
svm.Train(dataMat, resMat, null, null, param);
// Predict for each 300x300 pixel
using (Mat retPlot = Mat.Zeros(300, 300, MatType.CV_8UC3))
{
for (int x = 0; x < 300; x++)
{
for (int y = 0; y < 300; y++)
{
float[] sample = { x / 300f, y / 300f };
var sampleMat = new CvMat(1, 2, MatrixType.F32C1, sample);
int ret = (int)svm.Predict(sampleMat);
var plotRect = new CvRect(x, 300 - y, 1, 1);
if (ret == 1)
{
retPlot.Rectangle(plotRect, Scalar.Red);
}
else if (ret == 2)
{
retPlot.Rectangle(plotRect, Scalar.GreenYellow);
}
}
}
Window.ShowImages(retPlot);
}
}
}
/// <summary>
/// SVM
/// </summary>
/// <param name="dataFilename"></param>
/// <param name="filenameToSave"></param>
/// <param name="filenameToLoad"></param>
private void BuildSvmClassifier(string dataFilename, string filenameToSave, string filenameToLoad)
{
//C_SVCのパラメータ
const float SvmC = 1000;
//RBFカーネルのパラメータ
const float SvmGamma = 0.1f;
CvMat data = null;
CvMat responses = null;
CvMat sampleIdx = null;
int nsamplesAll = 0, ntrainSamples = 0;
double trainHr = 0, testHr = 0;
CvSVM svm = new CvSVM();
CvTermCriteria criteria = new CvTermCriteria(100, 0.001);
try
{
ReadNumClassData(dataFilename, 16, out data, out responses);
}
catch
{
Console.WriteLine("Could not read the database {0}", dataFilename);
return;
}
Console.WriteLine("The database {0} is loaded.", dataFilename);
nsamplesAll = data.Rows;
ntrainSamples = (int)(nsamplesAll * 0.2);
// Create or load Random Trees classifier
if (filenameToLoad != null)
{
// load classifier from the specified file
svm.Load(filenameToLoad);
ntrainSamples = 0;
if (svm.GetSupportVectorCount() == 0)
{
Console.WriteLine("Could not read the classifier {0}", filenameToLoad);
return;
}
Console.WriteLine("The classifier {0} is loaded.", filenameToLoad);
}
else
{
// create classifier by using <data> and <responses>
Console.Write("Training the classifier ...");
// 2. create sample_idx
sampleIdx = new CvMat(1, nsamplesAll, MatrixType.U8C1);
{
CvMat mat;
Cv.GetCols(sampleIdx, out mat, 0, ntrainSamples);
mat.Set(CvScalar.RealScalar(1));
Cv.GetCols(sampleIdx, out mat, ntrainSamples, nsamplesAll);
mat.SetZero();
}
// 3. train classifier
// 方法、カーネルにより使わないパラメータは0で良く、
// 重みについてもNULLで良い
svm.Train(data, responses, null, sampleIdx, new CvSVMParams(CvSVM.C_SVC, CvSVM.RBF, 0, SvmGamma, 0, SvmC, 0, 0, null, criteria));
Console.WriteLine();
}
// compute prediction error on train and test data
for (int i = 0; i < nsamplesAll; i++)
{
double r;
CvMat sample;
Cv.GetRow(data, out sample, i);
r = svm.Predict(sample);
// compare results
Console.WriteLine(
"predict: {0}, responses: {1}, {2}",
(char)r,
(char)responses.DataArraySingle[i],
Math.Abs((double)r - responses.DataArraySingle[i]) <= float.Epsilon ? "Good!" : "Bad!"
);
r = Math.Abs((double)r - responses.DataArraySingle[i]) <= float.Epsilon ? 1 : 0;
if (i < ntrainSamples)
{
trainHr += r;
}
else
{
testHr += r;
}
}
testHr /= (double)(nsamplesAll - ntrainSamples);
trainHr /= (double)ntrainSamples;
Console.WriteLine("Gamma={0:F5}, C={1:F5}", SvmGamma, SvmC);
if (filenameToLoad != null)
{
Console.WriteLine("Recognition rate: test = {0:F1}%", testHr * 100.0);
}
else
{
Console.WriteLine("Recognition rate: train = {0:F1}%, test = {1:F1}%", trainHr * 100.0, testHr * 100.0);
}
Console.WriteLine("Number of Support Vector: {0}", svm.GetSupportVectorCount());
// Save SVM classifier to file if needed
if (filenameToSave != null)
{
svm.Save(filenameToSave);
}
Console.Read();
if (sampleIdx != null)
{
sampleIdx.Dispose();
}
data.Dispose();
responses.Dispose();
svm.Dispose();
}
private void trainSVM(string[] shapeTypes)
{
string myPhotos = Environment.GetFolderPath(Environment.SpecialFolder.MyPictures);
string folderPath = Path.Combine(myPhotos, "shape_samples");
CvSVM svm = new CvSVM();
CvSVMParams svmParams = new CvSVMParams();
svmParams.SVMType = CvSVM.C_SVC;
svmParams.KernelType = CvSVM.LINEAR;
svmParams.C = 0.01;
int numSamples = Directory.GetFiles(folderPath, "*.png").Length;
int numFeatures = 0;
if (numSamples == 0)
{
return;
}
float[,] extractedFeatures = null;
float[] labels = new float[numSamples];
int i = 0;
foreach (string file in Directory.EnumerateFiles(folderPath, "*.png"))
{
Bitmap bitmap = (Bitmap)Image.FromFile(file, true);
Mat mat = BitmapConverter.ToMat(bitmap);
HOGDescriptor hog = new HOGDescriptor();
hog.WinSize = new OpenCvSharp.CPlusPlus.Size(32, 32); // Set hog features here: winSize; blockSize; cellSize
hog.BlockSize = new OpenCvSharp.CPlusPlus.Size(4, 4);
hog.CellSize = new OpenCvSharp.CPlusPlus.Size(4, 4);
hog.BlockStride = new OpenCvSharp.CPlusPlus.Size(2, 2);
float[] features = hog.Compute(mat, new OpenCvSharp.CPlusPlus.Size(16, 16), new OpenCvSharp.CPlusPlus.Size(0, 0), null);
if (extractedFeatures == null)
{
numFeatures = features.Length;
extractedFeatures = new float[numSamples, numFeatures];
}
for (int j = 0; j < numFeatures; j++)
{
extractedFeatures[i, j] = features[j];
}
labels[i] = -1;
for (int shape = 0; shape < shapeTypes.Length; shape++)
{
if (file.ToLower().Contains(shapeTypes[shape].ToLower()))
{
labels[i] = shape;
break;
}
}
bitmap.Dispose();
i++;
}
Mat labelsMat = new Mat(numSamples, 1, MatType.CV_32FC1, labels);
Mat extractedFeaturesMat = new Mat(numSamples, numFeatures, MatType.CV_32FC1, extractedFeatures);
try {
svm.Train(extractedFeaturesMat, labelsMat, null, null, svmParams);
svm.Save(Path.Combine(folderPath, "trained_svm"));
}
catch (OpenCVException e)
{
//Only a single class present
}
}
